{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T10:20:14Z","timestamp":1788862814431,"version":"build-2803163510"},"reference-count":58,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Current Problems in Diagnostic Radiology"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1067\/j.cpradiol.2025.04.011","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T11:00:57Z","timestamp":1744974057000},"page":"258-267","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"title":["FDA-approved artificial intelligence products in abdominal imaging: A comprehensive review"],"prefix":"10.1067","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8801-0235","authenticated-orcid":false,"given":"Pranav","family":"Ajmera","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Dillard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Kline","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Missert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Korfiatis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2728-7799","authenticated-orcid":false,"given":"Ashish","family":"Khandelwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1067\/j.cpradiol.2025.04.011_bib0001","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.ejrad.2018.06.020","article-title":"The past, present and future role of artificial intelligence in imaging","volume":"105","author":"Fazal","year":"2018","journal-title":"Eur J Radiol"},{"issue":"17","key":"10.1067\/j.cpradiol.2025.04.011_bib0002","doi-asserted-by":"crossref","first-page":"2760","DOI":"10.3390\/diagnostics13172760","article-title":"Redefining radiology: a review of artificial intelligence integration in medical imaging","volume":"13","author":"Najjar","year":"2023","journal-title":"Diagnostics"},{"issue":"7553","key":"10.1067\/j.cpradiol.2025.04.011_bib0003","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0004","unstructured":"Rajpurkar P., Irvin J., Zhu K., et al. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:171105225. 2017."},{"issue":"22","key":"10.1067\/j.cpradiol.2025.04.011_bib0005","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"jama"},{"issue":"8","key":"10.1067\/j.cpradiol.2025.04.011_bib0006","article-title":"Fda\u2019s comprehensive effort to advance new innovations: initiatives to modernize for innovation","volume":"29","author":"Gottlieb","year":"2018","journal-title":"FDA Voices"},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0007","unstructured":"FDA. The 510 (k) Program: Evaluating Substantial Equivalence in Premarket Notifications [510(k)] [Available from: https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/510k-program-evaluating-substantial-equivalence-premarket-notifications-510k."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0008","unstructured":"FDA. Proposed regulatory framework for modifications to artificial intelligence\/machine learning (AI\/ML)-based software as a medical device (SaMD) \u2013 Discussion paper and request for feedback. FDA2019. Available from: https:\/\/www.fda.gov\/files\/medical%20devices\/published\/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0011","unstructured":"FDA. Digital health Advisory Committee Meeting Announcement; [Available from: https:\/\/www.fda.gov\/advisory-committees\/advisory-committee-calendar\/november-20-21-2024-digital-health-advisory-committee-meeting-announcement-11202024."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0012","unstructured":"ACR. American College of Radiology Data Science Institute [Available from: https:\/\/aicentral.acrdsi.org\/."},{"issue":"6","key":"10.1067\/j.cpradiol.2025.04.011_bib0013","doi-asserted-by":"crossref","first-page":"672","DOI":"10.3748\/wjg.v25.i6.672","article-title":"Artificial intelligence in medical imaging of the liver","volume":"25","author":"Zhou","year":"2019","journal-title":"World J Gastroenterol"},{"issue":"9","key":"10.1067\/j.cpradiol.2025.04.011_bib0014","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.3390\/cancers13092162","article-title":"An overview of artificial intelligence applications in liver and pancreatic imaging","volume":"13","author":"Cardobi","year":"2021","journal-title":"Cancers"},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0015","unstructured":"Healthcare G. Hepatic vicar. Fast and reproducible method for segmenting the liver. [Available from: https:\/\/www.gehealthcare.com\/products\/advanced-visualization\/all-applications\/hepatic-vcar."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0016","unstructured":"SCS GMS, Hepatic VCAR: FDA; 2020 [K193281]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf19\/K193281.pdf."},{"issue":"1","key":"10.1067\/j.cpradiol.2025.04.011_bib0017","doi-asserted-by":"crossref","first-page":"9994","DOI":"10.1038\/s41598-019-46442-y","article-title":"Development of liver surface nodularity quantification program and its clinical application in nonalcoholic fatty liver disease","volume":"9","author":"Kim","year":"2019","journal-title":"Sci Rep"},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0018","unstructured":"Biometrics F.-.I. 510 (k) Premarket Notification- LSN 2020 [Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf20\/K201092.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0019","unstructured":"Liver Care- A comprehensive set of solutions [Available from: https:\/\/www.gehealthcare.com\/products\/interventional-image-guided-systems\/igs-for-interventional-radiology-and-oncology\/liver-care."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0020","unstructured":"Health R. FerriSmart [Available from: https:\/\/www.resonancehealth.com\/products\/ferrismart\/."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0021","unstructured":"Health R. Ferriscan-Ferrismart [Available from: https:\/\/www.resonancehealth.com\/products\/ferrismart\/."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0022","unstructured":"Ltd RHASP. FerriSmart Analysis System FDA; [K182218]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf18\/K182218.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0023","unstructured":"Ltd. P. Hepatica (Hepatica v1) 2021 [K203280]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf20\/K203280.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0024","unstructured":"Hepatica P. Improving accuracy of liver health and volume assessment to support better pre-surgical decision making for patients with liver cancer. [Available from: https:\/\/www.perspectum.com\/our-products\/hepatica."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0025","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s00534-005-0999-7","article-title":"Nomenclature of hepatic anatomy and resections: a review of the Brisbane 2000 system","volume":"12","author":"Strasberg","year":"2005","journal-title":"J Hepato-Biliary-Pancreat Surg"},{"issue":"2","key":"10.1067\/j.cpradiol.2025.04.011_bib0026","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1002\/hep.29797","article-title":"quantitative assessment of liver fat by MRI-PDFF as an endpoint in NASH trials","volume":"68","author":"Caussy","year":"2018","journal-title":"Hepatology"},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0027","unstructured":"Perspectum. MRI-PDFF of Liver Tissue as a Diagnostic Enrichment Biomarker 2018 [Available from: https:\/\/www.fda.gov\/media\/124105\/download#:\u223c:text=Date%3A%203.28.18-,Magnetic%20Resonance%20Imaging%20Proton%20Density%20Fat%20Fraction%20(MRI%2DPDFF),of%20lipids%20in%20the%20liver."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0028","unstructured":"Ltd P. Liver Multiscan v5: FDA; 2022 [K213960]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf21\/K213960.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0029","unstructured":"v5.0 P-L. Interpreting Liver Tissue Characterization for Physicians [Available from: https:\/\/assets-global.website-files.com\/64e75f5250f5c7e29babdeba\/6516055f97db1b405955d1eb_lmsv5-interpreting-liver-tissue-characterization-for-physicians.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0030","unstructured":"Ltd RHASP. Liver Iron Concentrattion Imaging Companion Diagnostic For Deferasirox: FDA; [K213776]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf21\/K213776.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0031","unstructured":"Health R. Liver Smart [Available from: https:\/\/www.resonancehealth.com\/products\/liversmart\/."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0032","unstructured":"Health R. Hepa Fat Smart [Available from: https:\/\/www.resonancehealth.com\/products\/hepafatsmart\/."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0033","unstructured":"Ltd RHASP. HepaFatSmart (V2.0.0): FDA; 2023 [K231459]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf23\/K231459.pdf."},{"issue":"4","key":"10.1067\/j.cpradiol.2025.04.011_bib0034","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.cgh.2019.08.023","article-title":"Volumetric liver fat fraction determines grade of steatosis more accurately than controlled attenuation parameter in patients with nonalcoholic fatty liver disease","volume":"18","author":"Harry","year":"2020","journal-title":"Clin Gastroenterol Hepatol"},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0035","unstructured":"SCS GMS. FlightPlan for Liver: FDA; 2021 [Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf21\/K210807.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0036","unstructured":"Perspectum. MRCP+ v1.0 FDA; [K183133]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf18\/K183133.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0037","unstructured":"Ltd. M. GIQuant: FDA; 2021 [K211356]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf21\/K211356.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0038","unstructured":"GI Quant [Radiology Health AI register]. Available from: https:\/\/radiology.healthairegister.com\/products\/motilent-giquant\/."},{"issue":"1","key":"10.1067\/j.cpradiol.2025.04.011_bib0039","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s13244-023-01421-w","article-title":"A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI","volume":"14","author":"Sun","year":"2023","journal-title":"Insights Imag"},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0040","unstructured":"HealthLytix) MISCd. RSI-MRI+ 2019 [K191278]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf19\/K191278.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0042","unstructured":"S.L. Q. qp-Prostate 2020 [K203582]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf20\/K203582.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0043","unstructured":"SCS GMS. PROView: FDA; [K193306]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf19\/K193306.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0044","unstructured":"P.C. AMISM. Advantis Platform: FDA; [K222975]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf22\/K222975.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0045","unstructured":"FDA. ProstatID 2022 [K212783]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf21\/K212783.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0048","unstructured":"Butterfly Network I. Ultrasonic pulsed echo imaging system [K200980]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf20\/K200980.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0049","unstructured":"Inc. E. Bladder AI (AIBV01): FDA; 2023 [K230497]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf23\/K230497.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0050","unstructured":"Viz. ai I. Viz AAA: FDA; 2023 [Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf22\/K223443.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0052","unstructured":"Ltd. AM. BriefCase [K222692]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf22\/K222692.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0053","unstructured":"Inc. C. ClariCT.AI: FDA; [Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf21\/K212074.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0054","unstructured":"AI Metrics L. AI Metrics: FDA; [Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf20\/K202229.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0055","unstructured":"3D Systems I. D2P [K183489]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf18\/K183489.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0056","series-title":"Aquilion Prime SP (TSX-303B8) V10.2 with AiCE-i","author":"Corporation","year":"2020"},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0057","unstructured":"Shanghai United Imaging Healthcare Co. L. Deep Recon: FDA; [Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf19\/K193073.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0058","unstructured":"uCT [Available from: https:\/\/usa.united-imaging.com\/products\/computed-tomography."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0059","unstructured":"Carestream Health I. Eclipse II with Smart Noise Cancellation 2021 [K213307]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf21\/K213307.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0060","unstructured":"Company C.H.C. Change Healthcare Anatomical A [K210719]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf21\/K210719.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0061","unstructured":"Inc. D. DeepLook PRECISE: FDA; 2021 [K202084]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf20\/K202084.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0062","unstructured":"Siemens Medical Solutions USA I. syngo.CT Applications 2022 [K220450]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf22\/K220450.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0064","unstructured":"GE Healthcare Japan Corporation-Deep Learning Image Reconstruction 2023 [K230807]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf23\/K230807.pdf."},{"key":"10.1067\/j.cpradiol.2025.04.011_bib0065","unstructured":"Ltd. P. CoverScan (CoverScan v1.1) 2023 [K230294]. Available from: https:\/\/www.accessdata.fda.gov\/cdrh_docs\/pdf23\/K230294.pdf."}],"container-title":["Current Problems in Diagnostic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0363018825000829?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0363018825000829?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T19:32:19Z","timestamp":1770060739000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0363018825000829"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":58,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["S0363018825000829"],"URL":"https:\/\/doi.org\/10.1067\/j.cpradiol.2025.04.011","relation":{},"ISSN":["0363-0188"],"issn-type":[{"value":"0363-0188","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FDA-approved artificial intelligence products in abdominal imaging: A comprehensive review","name":"articletitle","label":"Article Title"},{"value":"Current Problems in Diagnostic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1067\/j.cpradiol.2025.04.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}